Immersive virtual reality (VR) enables naturalistic neuroscientific studies while maintaining experimental control, but dynamic and interactive stimuli pose methodological challenges. We here probed the link between emotional arousal, a fundamental property of affective experience, and parieto-occipital alpha power under naturalistic stimulation: 37 young healthy adults completed an immersive VR experience, which included rollercoaster rides, while their EEG was recorded. They then continuously rated their subjective emotional arousal while viewing a replay of their experience. The association between emotional arousal and parieto-occipital alpha power was tested and confirmed by (1) decomposing the continuous EEG signal while maximizing the comodulation between alpha power and arousal ratings and by (2) decoding periods of high and low arousal with discriminative common spatial patterns and a Long Short-Term Memory recurrent neural network. We successfully combine EEG and a naturalistic immersive VR experience to extend previous findings on the neurophysiology of emotional arousal towards real-world neuroscience.

Immersive virtual reality (VR) enables naturalistic neuroscientific studies while maintaining experimental control, but dynamic and interactive stimuli pose methodological challenges. We here probed the link between emotional arousal, a fundamental property of affective experience, and parieto-occipital alpha power under naturalistic stimulation: 37 young healthy adults completed an immersive VR experience, which included rollercoaster rides, while their EEG was recorded. They then continuously rated their subjective emotional arousal while viewing a replay of their experience. The association between emotional arousal and parieto-occipital alpha power was tested and confirmed by (1) decomposing the continu- ous EEG signal while maximizing the comodulation between alpha power and arousal ratings and by (2) decoding periods of high and low arousal with discriminative common spatial pat- terns and a Long Short-Term Memory recurrent neural network. We successfully combine EEG and a naturalistic immersive VR experience to extend previous findings on the neurophysiology of emotional arousal towards real-world neuroscience.

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Human experience is characterised by an apparently continuous stream of information. However, when recalling our past, we commonly experience somewhat discrete episodes. Event segmentation has been suggested to provide a basis for episodic memory formation and recognition/recall. Previous work has suggested that event boundaries are reflected in shifts in stable patterns of neural activity across processing hierarchies, and that these boundaries can be detected using neuroimaging techniques. From a different direction, a recent series of studies in the domain of human time perception have shown that tracking event boundaries as "surprise" in network activity - in human perceptual processing or neural network proxies - successfully describes subjective reports of time on the scale of seconds to minutes. This project aims to understand how these methods of obtaining “event boundaries” perform relative to human-provided event annotations, in situations of life-like, naturalistic sensory stimulation. We compared the boundaries estimated from our neural-network-based model of time perception and a data-driven event segmentation approach (Greedy State Boundary Search on EEG data), against annotations from 131 human raters using two different inferential processes. In both methods, event boundaries from our model-based and the EEG data-driven approach performed similarly well, and were both indistinguishable from human provided boundaries in at least ~89% of cases examined. A random boundary generator was similar to human raters in only approximately 13% of cases. This work demonstrates the potential to reconcile the fields of time perception and episodic memory through a common foundation in the processes underlying event segmentation.